1. Technical Field
The present disclosure relates to an image processing method, and more particularly, to an image processing method and system using gain-controllable clipped histogram equalization (GCCHE) to enhance image contrast.
2. Discussion of the Related Art
An original image generated in an imaging system, for example, in a digital camera or a camcorder, may not have clear or well defined contrast. When the contrast is not clear, the sharpness of the image decreases. Accordingly, to increase image sharpness, many developments for enhancing contrast have been made.
Histogram equalization (HE) is one of the most widely used methods of enhancing contrast effectively and simply. To distinguish basic HE from other modified methods, the basic HE is referred to as standard HE (SHE). SHE is based on a technique of regenerating a brightness histogram having uniform distribution and is widely used to process images like medical images and infrared images.
However, SHE may substantially change the brightness of an input image and increase undesirable noise and, therefore, special facts should be additionally considered when SHE is used in products like TV receivers and camcorders. In other words, to use SHE in practical products, a technique for increasing contrast while maintaining mean brightness is required. Such techniques may be divided into methods using histogram processing and methods using a spatio-temporal filter. The methods using histogram processing include bi-histogram equalization (BHE), recursive mean-separate histogram equalization (RMSHE), and clipped histogram equalization (CHE).
A BHE algorithm is provided to overcome a problem of SHE in which image brightness considerably changes. In the BHE algorithm, a histogram is segmented into two regions and HE is separately performed on each of the two regions. However, since HE is separately performed on the two divided regions, it may be difficult to increase contrast with respect to objects and backgrounds. To overcome this problem, an RMSHE algorithm is used. The RMSHE algorithm is an expansion of the BHE algorithm. In the RMSHE algorithm, four histograms are generated using a mean brightness value. However, the amount of computation and complexity also increases when the number of repetitions increases, and therefore, it is necessary to control the number of repetitions. Since the above-described methods use segmentation, they are is not effective to improve images in terms of noise or contrast. In other words, noise and contrast should be considered based on information on pixels distributed in an image not the distribution of a histogram, and CHE is a representative method.
In a CHE algorithm, a maximum value is set for a brightness histogram, an upper part of the histogram exceeding the maximum value is clipped, and a clipped region is reset with respect to an entire region, thereby limiting the maximum value of the histogram. Since CHE does not actually clip a histogram but is used only in computation, it does not destroy information in an image. However, it is usual that the conventional CHE algorithm has a fixed threshold and, therefore, it is difficult to adaptively control a clipped region according to the brightness of an image. Accordingly, image characteristics cannot be considered, resulting in bad effects in some images. Moreover, even though the CHE algorithm is robust to noise because a clipped histogram region is reset with respect to an entire histogram region, noise may increase in a part, such as a black level region, of an image.